AUTHOR=Monti Ricardo P. , Lorenz Romy , Hellyer Peter , Leech Robert , Anagnostopoulos Christoforos , Montana Giovanni TITLE=Decoding Time-Varying Functional Connectivity Networks via Linear Graph Embedding Methods JOURNAL=Frontiers in Computational Neuroscience VOLUME=11 YEAR=2017 URL=https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2017.00014 DOI=10.3389/fncom.2017.00014 ISSN=1662-5188 ABSTRACT=

An exciting avenue of neuroscientific research involves quantifying the time-varying properties of functional connectivity networks. As a result, many methods have been proposed to estimate the dynamic properties of such networks. However, one of the challenges associated with such methods involves the interpretation and visualization of high-dimensional, dynamic networks. In this work, we employ graph embedding algorithms to provide low-dimensional vector representations of networks, thus facilitating traditional objectives such as visualization, interpretation and classification. We focus on linear graph embedding methods based on principal component analysis and regularized linear discriminant analysis. The proposed graph embedding methods are validated through a series of simulations and applied to fMRI data from the Human Connectome Project.